16,027 research outputs found
BDGS: A Scalable Big Data Generator Suite in Big Data Benchmarking
Data generation is a key issue in big data benchmarking that aims to generate
application-specific data sets to meet the 4V requirements of big data.
Specifically, big data generators need to generate scalable data (Volume) of
different types (Variety) under controllable generation rates (Velocity) while
keeping the important characteristics of raw data (Veracity). This gives rise
to various new challenges about how we design generators efficiently and
successfully. To date, most existing techniques can only generate limited types
of data and support specific big data systems such as Hadoop. Hence we develop
a tool, called Big Data Generator Suite (BDGS), to efficiently generate
scalable big data while employing data models derived from real data to
preserve data veracity. The effectiveness of BDGS is demonstrated by developing
six data generators covering three representative data types (structured,
semi-structured and unstructured) and three data sources (text, graph, and
table data)
Object Database Scalability for Scientific Workloads
We describe the PetaByte-scale computing challenges posed by the next generation of particle physics experiments, due to start operation in 2005. The computing models adopted by the experiments call for systems capable of handling sustained data acquisition rates of at least 100 MBytes/second into an Object Database, which will have to handle several PetaBytes of accumulated data per year. The systems will be used to schedule CPU intensive reconstruction and analysis tasks on the highly complex physics Object data which need then be served to clients located at universities and laboratories worldwide. We report on measurements with a prototype system that makes use of a 256 CPU HP Exemplar X Class machine running the Objectivity/DB database. Our results show excellent scalability for up to 240 simultaneous database clients, and aggregate I/O rates exceeding 150 Mbytes/second, indicating the viability of the computing models
Performance and scalability of indexed subgraph query processing methods
Graph data management systems have become very popular
as graphs are the natural data model for many applications.
One of the main problems addressed by these systems is subgraph
query processing; i.e., given a query graph, return all
graphs that contain the query. The naive method for processing
such queries is to perform a subgraph isomorphism
test against each graph in the dataset. This obviously does
not scale, as subgraph isomorphism is NP-Complete. Thus,
many indexing methods have been proposed to reduce the
number of candidate graphs that have to underpass the subgraph
isomorphism test. In this paper, we identify a set of
key factors-parameters, that influence the performance of
related methods: namely, the number of nodes per graph,
the graph density, the number of distinct labels, the number
of graphs in the dataset, and the query graph size. We then
conduct comprehensive and systematic experiments that analyze
the sensitivity of the various methods on the values of
the key parameters. Our aims are twofold: first to derive
conclusions about the algorithms’ relative performance, and,
second, to stress-test all algorithms, deriving insights as to
their scalability, and highlight how both performance and
scalability depend on the above factors. We choose six wellestablished
indexing methods, namely Grapes, CT-Index,
GraphGrepSX, gIndex, Tree+∆, and gCode, as representative
approaches of the overall design space, including the
most recent and best performing methods. We report on
their index construction time and index size, and on query
processing performance in terms of time and false positive
ratio. We employ both real and synthetic datasets. Specifi-
cally, four real datasets of different characteristics are used:
AIDS, PDBS, PCM, and PPI. In addition, we generate a
large number of synthetic graph datasets, empowering us to
systematically study the algorithms’ performance and scalability
versus the aforementioned key parameters
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